Adaptive depth and receptive field selection network for defect semantic segmentation on castings X-rays-Reference-Cited by-同舟云学术

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Adaptive depth and receptive field selection network for defect semantic segmentation on castings X-rays

Published:2020-12 Issue: Volume:116 Page:102345
ISSN:0963-8695
Container-title:NDT & E International
language:en
Short-container-title:NDT & E International

Author:

Yu Han,Li Xingjie,Song Kechen,Shang Erfeng,Liu Huiyu,Yan Yunhui

Funder

Key Research and Development Program of Liaoning Province

Publisher

Elsevier BV

Subject

Mechanical Engineering,Condensed Matter Physics,General Materials Science

Reference53 articles.

1. Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence [J];Mery;IEEE Trans Robot Autom,2002

2. Automated defect detection in aluminum castings and welds using neuro-fuzzy classifiers. [C]//;Hernandez,2008

3. Improving automatic detection of defects in castings by applying wavelet technique [J];Li;IEEE Trans Ind Electron,2006

4. A sparse-representation-based robust inspection system for hidden defects classification in casting components [J];Zhao;Neurocomputing,2015

5. Application of RNAMlet to surface defect identification of steels [J];Xu;Optic Laser Eng,2018

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